SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification

Abstract

The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated well with the help of EEG signals. Goal : In this paper, two versatile deep learning methods are proposed for the efficient classification of epilepsy and schizophrenia from EEG datasets. Methods : The main advantage of using deep learning when compared to other machine learning algorithms is that it has the capability to accomplish feature engineering on its own. Swarm intelligence is also a highly useful technique to solve a wide range of real-world, complex, and non-linear problems. Therefore, taking advantage of these factors, the first method proposed is a Sparse Autoencoder (SAE) with swarm based deep learning method and it is named as (SASDL) using Particle Swarm Optimization (PSO) technique, Cuckoo Search Optimization (CSO) technique and Bat Algorithm (BA) technique; and the second technique proposed is the Reinforcement Learning based on Bidirectional Long-Short Term Memory (BiLSTM), Attention Mechanism, Tree LSTM and Q learning, and it is named as (RBATQ) technique. Results and Conclusions : Both these two novel deep learning techniques are tested on epilepsy and schizophrenia EEG datasets and the results are analyzed comprehensively, and a good classification accuracy of more than 93% is obtained for all the datasets.

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

“…Similarly, another recent study [24] employed a deep convolutional autoencoder and bidirectional long short memory for epileptic seizure detection (DCAE-ESD-Bi-LSTM) for the same task, and achieved more accurate (99.8%) and optimized results (99.9% precision and 99.6% F1 score). A sparse autoencoder with a swarm-based DL method known as (SASDL) employing PSO, was proposed, which achieved an accuracy [25] of 98.5%.…”

IoT‐Based Reinforcement Learning Using Probabilistic Model for Determining Extensive Exploration through Computational Intelligence for Next‐Generation Techniques

“…Some researchers have proposed a policy learning algorithm [ 25 ], which mainly solves the problem of continuous action space, such as robot control, but when the size of the policy space is exponential, this method cannot guarantee the efficiency of exploration. Another class of approaches encourages exploration by assigning rewards to infrequently visited states based on pseudocounts [ 26 ] or density models [ 27 ].…”

A Detailed Analysis & Parametric Comparison Of Eeg Processing Models

“…Studies mentioned in [12] suggest that EEG scans may reveal the electrical activity of the brain. Only a few neurological diseases, such as epilepsy, schizophrenia, sleep issues, and Parkinson's disease, may be diagnosed and evaluated with the use of EEG signals.…”

“…In addition, it is now possible to get your hands on some portable EEG acquisition gear. For example, emotive has seen significant use in the field of brain-computer interface [11][12][13] due to its low cost, mobility, and performance that is comparable to those of medical devices. However, despite the fact that a great deal of medical gear and portable EEG collecting devices generate the EEG data, which can be used for research on epilepsy, there are no standard data formats because there are so many different sources of data.…”